Hi Cheolsoo, Thanks - I will try this now and get back to you.
Out of interest; could you explain (or point me towards resources that would) why the combiner would be a problem? Also, could the fact that Pig builds an intermediary data structure (?) whilst Hive just performs a sort then the arithmetic operation explain the slowdown? (Apologies, I'm quite new to Pig/Hive - just my guesses). Regards, Benjamin On 22 August 2013 01:07, Cheolsoo Park <[email protected]> wrote: > Hi Benjamin, > > Thank you very much for sharing detailed information! > > 1) From the runtime numbers that you provided, the mappers are very slow. > > CPU time spent (ms)5,081,610168,7405,250,350CPU time spent (ms)5,052,700 > 178,2205,230,920CPU time spent (ms)5,084,430193,4805,277,910 > > 2) In your GROUP BY query, you have an algebraic UDF "COUNT". > > I am wondering whether disabling combiner will help here. I have seen a lot > of cases where combiner actually hurt performance significantly if it > doesn't combine mapper outputs significantly. Briefly looking at > generate_data.pl in PIG-200, it looks like a lot of random keys are > generated. So I guess you will end up with a large number of small bags > rather than a small number of large bags. If that's the case, combiner will > only add overhead to mappers. > > Can you try to include this "set pig.exec.nocombiner true;" and see whether > it helps? > > Thanks, > Cheolsoo > > > > > > > On Wed, Aug 21, 2013 at 3:52 AM, Benjamin Jakobus <[email protected] > >wrote: > > > Hi Cheolsoo, > > > > >>What's your query like? Can you share it? Do you call any algebraic UDF > > >> after group by? I am wondering whether combiner matters in your test. > > I have been running 3 different types of queries. > > > > The first was performed on datasets of 6 different sizes: > > > > > > - Dataset size 1: 30,000 records (772KB) > > - Dataset size 2: 300,000 records (6.4MB) > > - Dataset size 3: 3,000,000 records (63MB) > > - Dataset size 4: 30 million records (628MB) > > - Dataset size 5: 300 million records (6.2GB) > > - Dataset size 6: 3 billion records (62GB) > > > > The datasets scale linearly, whereby the size equates to 3000 * 10n . > > A seventh dataset consisting of 1,000 records (23KB) was produced to > > perform join > > operations on. Its schema is as follows: > > name - string > > marks - integer > > gpa - float > > The data was generated using the generate data.pl perl script available > > for > > download > > from https://issues.apache.org/jira/browse/PIG-200 to produce the > > datasets. The results are as follows: > > > > > > * * * * * * *Set 1 * *Set 2** * *Set 3** * > > *Set > > 4** * *Set 5** * *Set 6* > > *Arithmetic** * 32.82* * 36.21* * 49.49* * 83.25* > > * > > 423.63* * 3900.78 > > *Filter 10%** * 32.94* * 34.32* * 44.56* * 66.68* > > * > > 295.59* * 2640.52 > > *Filter 90%** * 33.93* * 32.55* * 37.86* * 53.22* > > * > > 197.36* * 1657.37 > > *Group** * * *49.43* * 53.34* * 69.84* * 105.12* > > *497.61* * 4394.21 > > *Join** * * * 49.89* * 50.08* * 78.55* * > 150.39* > > *1045.34* *10258.19 > > *Averaged performance of arithmetic, join, group, order, distinct select > > and filter operations on six datasets using Pig. Scripts were configured > as > > to use 8 reduce and 11 map tasks.* > > > > > > > > * * * Set 1** * *Set 2** * *Set 3** * > > *Set > > 4** * *Set 5** * *Set 6* > > *Arithmetic** * 32.84* * 37.33* * 72.55* * 300.08 > > 2633.72 27821.19 > > *Filter 10% * 32.36* * 53.28* * 59.22* * 209.5* > * > > 1672.3* *18222.19 > > *Filter 90% * 31.23* * 32.68* * 36.8* * 69.55* > > * > > 331.88* *3320.59 > > *Group * * * 48.27* * 47.68* * 46.87* * 53.66* > > *141.36* *1233.4 > > *Join * * * * *48.54* *56.86* * 104.6* * > 517.5* > > * 4388.34* * - > > *Distinct** * * *48.73* *53.28* * 72.54* * > 109.77* > > * - * * * * - > > *Averaged performance of arithmetic, join, group, distinct select and > > filter operations on six datasets using Hive. Scripts were configured as > to > > use 8 reduce and 11 map tasks.* > > > > (If you want to see the standard deviation, let me know). > > > > So, to summarize the results: Pig outperforms Hive, with the exception of > > using *Group By*. > > > > The Pig scripts used for this benchmark are as follows: > > *Arithmetic* > > -- Generate with basic arithmetic > > A = load '$input/dataset_300000000' using PigStorage('\t') as (name, age, > > gpa) PARALLEL $reducers; > > B = foreach A generate age * gpa + 3, age/gpa - 1.5 PARALLEL $reducers; > > store B into '$output/dataset_300000000_projection' using PigStorage() > > PARALLEL $reducers; > > > > * > > * > > *Filter 10%* > > -- Filter that removes 10% of data > > A = load '$input/dataset_300000000' using PigStorage('\t') as (name, age, > > gpa) PARALLEL $reducers; > > B = filter A by gpa < '3.6' PARALLEL $reducers; > > store B into '$output/dataset_300000000_filter_10' using PigStorage() > > PARALLEL $reducers; > > > > > > *Filter 90%* > > -- Filter that removes 90% of data > > A = load '$input/dataset_300000000' using PigStorage('\t') as (name, age, > > gpa) PARALLEL $reducers; > > B = filter A by age < '25' PARALLEL $reducers; > > store B into '$output/dataset_300000000_filter_90' using PigStorage() > > PARALLEL $reducers; > > > > * > > * > > *Group* > > A = load '$input/dataset_300000000' using PigStorage('\t') as (name, age, > > gpa) PARALLEL $reducers; > > B = group A by name PARALLEL $reducers; > > C = foreach B generate flatten(group), COUNT(A.age) PARALLEL $reducers; > > store C into '$output/dataset_300000000_group' using PigStorage() > PARALLEL > > $reducers; > > * > > * > > *Join* > > A = load '$input/dataset_300000000' using PigStorage('\t') as (name, age, > > gpa) PARALLEL $reducers; > > B = load '$input/dataset_join' using PigStorage('\t') as (name, age, gpa) > > PARALLEL $reducers; > > C = cogroup A by name inner, B by name inner PARALLEL $reducers; > > D = foreach C generate flatten(A), flatten(B) PARALLEL $reducers; > > store D into '$output/dataset_300000000_cogroup_big' using PigStorage() > > PARALLEL $reducers; > > > > Similarly, here the Hive scripts: > > *Arithmetic* > > SELECT (dataset.age * dataset.gpa + 3) AS F1, (dataset.age/dataset.gpa - > > 1.5) AS F2 > > FROM dataset > > WHERE dataset.gpa > 0; > > > > *Filter 10%* > > SELECT * > > FROM dataset > > WHERE dataset.gpa < 3.6; > > > > *Filter 90%* > > SELECT * > > FROM dataset > > WHERE dataset.age < 25; > > > > *Group* > > SELECT COUNT(dataset.age) > > FROM dataset > > GROUP BY dataset.name; > > > > *Join* > > SELECT * > > FROM dataset JOIN dataset_join > > ON dataset.name = dataset_join.name; > > > > I will re-run the benchmarks to see whether it is the reduce or map side > > that is slower and get back to you later today. > > > > The other two benchmarks were slightly different: I performed transitive > > self joins in which Pig outperformed Hive. However once I added a Group > By, > > Hive began outperforming Pig. > > > > I also ran the TPC-H benchmarks and noticed that Hive (surprisingly) > > outperformed Pig. However what *seems* to cause the actual performance > > difference is the heavy usage of the Group By operator in all but 3 TPC-H > > test scripts. > > > > Re-running the scripts whilst omitting the the grouping of data produces > > the expected results. For example, running script 3 > > (q3_shipping_priority.pig) whilst omitting the Group By operator > > significantly reduces the runtime (to 1278.49 seconds real time runtime > or > > a total of 12,257,630ms CPU time). > > > > The fact that the Group By operator skews the TPC-H benchmark in favour > of > > Apache Hive is supported by further experiments: as noted earlier a > > benchmark was carried out on a transitive self-join. The former took Pig > an > > average of 45.36 seconds (real time runtime) to execute; it took Hive > 56.73 > > seconds. The latter took Pig 157.97 and Hive 180.19 seconds (again, on > > average). However adding the Group By operator to the scripts turned the > > tides: Pig is now significantly slower than Hive, requiring an average of > > 278.15 seconds. Hive on the other hand required only 204.01 to perform > the > > JOIN and GROUP operations. > > > > Real time runtime is measured using the time -p command. > > > > Best Regards, > > Benjamin > > > > > > > > On 20 August 2013 19:56, Cheolsoo Park <[email protected]> wrote: > > > > > Hi Benjarmin, > > > > > > Can you describe which step of group by is slow? Mapper side or reducer > > > side? > > > > > > What's your query like? Can you share it? Do you call any algebraic UDF > > > after group by? I am wondering whether combiner matters in your test. > > > > > > Thanks, > > > Cheolsoo > > > > > > > > > > > > > > > On Tue, Aug 20, 2013 at 2:27 AM, Benjamin Jakobus < > > [email protected] > > > >wrote: > > > > > > > Hi all, > > > > > > > > After benchmarking Hive and Pig, I found that the Group By operator > in > > > Pig > > > > is drastically slower that Hive's. I was wondering whether anybody > has > > > > experienced the same? And whether people may have any tips for > > improving > > > > the performance of this operation? (Adding a DISTINCT as suggested by > > an > > > > earlier post on here doesn't help. I am currently re-running the > > > benchmark > > > > with LZO compression enabled). > > > > > > > > Regards, > > > > Ben > > > > > > > > > >
